from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

daal4py_KMeans_short: 0h 0m 1s

daal4py_Ridge: 0h 0m 2s

KMeans_short: 0h 0m 3s

daal4py_LogisticRegression: 0h 0m 4s

daal4py_KMeans_tall: 0h 0m 8s

Ridge: 0h 0m 11s

LogisticRegression: 0h 0m 21s

KMeans_tall: 0h 0m 24s

daal4py_KNeighborsClassifier_kd_tree: 0h 0m 29s

KNeighborsClassifier_kd_tree: 0h 2m 50s

daal4py_KNeighborsClassifier: 0h 2m 52s

HistGradientBoostingClassifier: 0h 5m 2s

lightgbm: 0h 5m 5s

catboost_lossguide: 0h 5m 9s

xgboost: 0h 5m 15s

catboost_symmetric: 0h 6m 34s

KNeighborsClassifier: 0h 36m 16s

total: 1h 10m 54s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.257	0.000	3.118	0.000	1	100	NaN	NaN	0.500	0.000	0.513	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.395	0.104	0.000	0.025	1	100	0.931	0.944	2.111	0.020	12.028	0.126	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.197	0.002	0.000	0.197	1	100	1.000	1.000	0.084	0.000	2.352	0.032	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.132	0.000	6.056	0.000	1	5	NaN	NaN	0.477	0.000	0.277	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.457	0.071	0.000	0.025	1	5	0.814	0.821	2.053	0.076	12.399	0.462	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.193	0.001	0.000	0.193	1	5	1.000	1.000	0.085	0.001	2.286	0.039	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.126	0.000	6.346	0.000	-1	5	NaN	NaN	0.485	0.000	0.260	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	36.886	0.000	0.000	0.037	-1	5	0.814	0.821	2.035	0.024	18.125	0.210	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.182	0.020	0.000	0.182	-1	5	1.000	1.000	0.084	0.001	2.171	0.235	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.129	0.000	6.203	0.000	-1	100	NaN	NaN	0.487	0.000	0.265	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	36.797	0.000	0.000	0.037	-1	100	0.931	0.696	2.029	0.007	18.134	0.067	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.186	0.009	0.000	0.186	-1	100	1.000	1.000	0.084	0.001	2.218	0.111	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.131	0.000	6.129	0.000	1	1	NaN	NaN	0.487	0.000	0.268	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	13.341	0.037	0.000	0.013	1	1	0.702	0.944	2.122	0.025	6.288	0.076	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.182	0.003	0.000	0.182	1	1	1.000	1.000	0.085	0.002	2.140	0.060	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.126	0.000	6.349	0.000	-1	1	NaN	NaN	0.486	0.000	0.259	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.810	0.132	0.000	0.025	-1	1	0.702	0.696	2.074	0.037	11.962	0.223	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.161	0.017	0.000	0.161	-1	1	1.000	1.000	0.091	0.008	1.768	0.241	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.000	0.261	0.000	1	100	NaN	NaN	0.117	0.000	0.525	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	23.561	0.019	0.000	0.024	1	100	0.987	0.981	0.380	0.002	62.063	0.390	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.022	0.001	0.000	0.022	1	100	1.000	1.000	0.007	0.000	3.197	0.195	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.000	0.266	0.000	1	5	NaN	NaN	0.117	0.000	0.513	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	23.488	0.020	0.000	0.023	1	5	0.988	0.979	0.323	0.006	72.657	1.269	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.022	0.001	0.000	0.022	1	5	1.000	1.000	0.006	0.001	3.427	0.339	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.000	0.263	0.000	-1	5	NaN	NaN	0.116	0.000	0.522	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	34.766	0.000	0.000	0.035	-1	5	0.988	0.979	0.337	0.009	103.249	2.756	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.026	0.001	0.000	0.026	-1	5	1.000	1.000	0.007	0.001	3.965	0.452	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.000	0.264	0.000	-1	100	NaN	NaN	0.118	0.000	0.514	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	34.837	0.000	0.000	0.035	-1	100	0.987	0.973	0.322	0.002	108.287	0.577	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.030	0.004	0.000	0.030	-1	100	1.000	1.000	0.006	0.000	4.704	0.655	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.000	0.260	0.000	1	1	NaN	NaN	0.116	0.000	0.529	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.314	0.015	0.000	0.010	1	1	0.982	0.981	0.380	0.004	27.109	0.298	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.000	0.000	0.015	1	1	1.000	1.000	0.007	0.000	2.284	0.130	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.000	0.263	0.000	-1	1	NaN	NaN	0.116	0.000	0.522	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.862	0.094	0.000	0.022	-1	1	0.982	0.973	0.329	0.013	66.485	2.617	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.002	0.000	0.020	-1	1	1.000	1.000	0.006	0.000	3.277	0.428	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.410	0.000	0.023	0.000	1	1	NaN	NaN	0.744	0.000	4.581	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.796	0.006	0.000	0.001	1	1	0.948	0.960	0.111	0.001	7.153	0.091	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.001	0.000	0.002	1	1	1.000	1.000	0.000	0.000	8.519	4.268	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.326	0.000	0.024	0.000	-1	5	NaN	NaN	0.743	0.000	4.475	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.891	0.013	0.000	0.001	-1	5	0.963	0.973	0.577	0.004	1.543	0.024	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.006	0.001	0.000	0.006	-1	5	0.000	1.000	0.001	0.000	5.233	1.786	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.373	0.000	0.024	0.000	1	5	NaN	NaN	0.749	0.000	4.501	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.496	0.008	0.000	0.001	1	5	0.963	0.973	0.574	0.003	2.608	0.021	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.003	0.000	0.005	1	5	0.000	1.000	0.001	0.000	4.007	2.642	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.409	0.000	0.023	0.000	1	100	NaN	NaN	0.751	0.000	4.540	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.950	0.019	0.000	0.005	1	100	0.960	0.972	0.199	0.002	24.823	0.305	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.007	0.001	0.000	0.007	1	100	0.000	1.000	0.000	0.000	17.940	6.989	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.355	0.000	0.024	0.000	-1	100	NaN	NaN	0.753	0.000	4.456	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.908	0.022	0.000	0.003	-1	100	0.960	0.960	0.110	0.002	26.335	0.417	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.010	0.001	0.000	0.010	-1	100	0.000	1.000	0.000	0.000	39.618	16.762	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.334	0.000	0.024	0.000	-1	1	NaN	NaN	0.749	0.000	4.451	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.485	0.010	0.000	0.000	-1	1	0.948	0.972	0.200	0.003	2.420	0.063	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	1	1.000	1.000	0.000	0.000	10.521	4.355	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.883	0.000	0.018	0.000	1	1	NaN	NaN	0.520	0.000	1.697	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.027	0.001	0.001	0.000	1	1	0.977	0.972	0.001	0.000	33.492	9.986	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.677	3.993	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.870	0.000	0.018	0.000	-1	5	NaN	NaN	0.525	0.000	1.658	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.033	0.004	0.000	0.000	-1	5	0.988	0.982	0.008	0.001	4.399	0.594	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	19.516	13.177	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.865	0.000	0.018	0.000	1	5	NaN	NaN	0.526	0.000	1.645	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.029	0.000	0.001	0.000	1	5	0.988	0.982	0.007	0.000	3.882	0.244	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.181	3.446	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.878	0.000	0.018	0.000	1	100	NaN	NaN	0.579	0.000	1.516	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.059	0.000	0.000	0.000	1	100	0.991	0.980	0.001	0.000	43.902	16.108	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.698	3.814	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.869	0.000	0.018	0.000	-1	100	NaN	NaN	0.562	0.000	1.547	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.051	0.001	0.000	0.000	-1	100	0.991	0.972	0.001	0.000	54.242	16.028	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	27.568	21.172	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.908	0.000	0.018	0.000	-1	1	NaN	NaN	0.555	0.000	1.637	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.033	0.004	0.000	0.000	-1	1	0.977	0.980	0.001	0.001	22.476	8.664	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	20.342	14.136	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.682	0.0	0.703	0.000	random	NaN	30	NaN	0.468	0.0	1.458	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.0	0.316	0.000	random	0.001	30	0.001	0.000	0.0	8.995	5.487	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	11.189	7.387	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.657	0.0	0.731	0.000	k-means++	NaN	30	NaN	0.554	0.0	1.186	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.0	0.318	0.000	k-means++	0.001	30	0.001	0.000	0.0	8.804	4.785	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	11.239	7.076	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.548	0.0	3.665	0.000	random	NaN	30	NaN	2.905	0.0	2.254	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	12.806	0.000	random	0.002	30	0.002	0.000	0.0	6.602	2.504	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.017	0.001	random	1.000	30	1.000	0.000	0.0	10.822	6.472	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.528	0.0	3.676	0.000	k-means++	NaN	30	NaN	3.010	0.0	2.168	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	12.911	0.000	k-means++	0.002	30	0.001	0.000	0.0	4.673	1.319	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.016	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.395	4.533	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.093	0.000	0.034	0.000	random	NaN	20	NaN	0.038	0.0	2.460	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.156	0.000	random	0.002	20	0.003	0.001	0.0	2.745	0.481	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.884	5.820	See	See
3	KMeans_short	fit	10000	10000	2	20	0.273	0.000	0.012	0.000	k-means++	NaN	20	NaN	0.109	0.0	2.500	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.156	0.000	k-means++	0.003	20	0.001	0.001	0.0	2.724	0.616	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.001	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	12.069	8.823	See	See
6	KMeans_short	fit	10000	10000	100	20	0.259	0.000	0.619	0.000	random	NaN	20	NaN	0.169	0.0	1.534	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.000	5.395	0.000	random	0.359	20	0.303	0.001	0.0	2.228	0.560	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.010	0.002	random	1.000	20	1.000	0.000	0.0	8.478	4.076	See	See
9	KMeans_short	fit	10000	10000	100	20	0.715	0.000	0.224	0.000	k-means++	NaN	20	NaN	0.394	0.0	1.817	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.000	5.627	0.000	k-means++	0.324	20	0.312	0.001	0.0	2.279	0.321	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.001	0.008	0.002	k-means++	1.000	20	1.000	0.000	0.0	9.169	6.354	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.721	0.0	[-0.10066661]	0.000	NaN	NaN	NaN	NaN	NaN	1.974	0.0	5.937	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[47.94809139]	0.000	NaN	NaN	NaN	NaN	0.518	0.000	0.0	0.909	0.424	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.18362037]	0.000	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.442	0.330	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.866	0.0	[2.40048964]	0.001	NaN	NaN	NaN	NaN	NaN	0.753	0.0	1.150	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[111.63860951]	0.000	NaN	NaN	NaN	NaN	0.250	0.003	0.0	0.561	0.121	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[18.29885728]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.149	0.086	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.193	0.0	0.415	0.0	NaN	NaN	NaN	0.195	0.0	0.991	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.0	8.052	0.0	NaN	NaN	0.1	0.016	0.0	0.605	0.019	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.095	0.0	NaN	NaN	NaN	0.000	0.0	0.606	0.557	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.436	0.0	0.557	0.0	NaN	NaN	NaN	0.265	0.0	5.409	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	5.316	0.0	NaN	NaN	1.0	0.000	0.0	0.629	0.469	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.012	0.0	NaN	NaN	NaN	0.000	0.0	0.637	0.603	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(config="config.yml")
reporting_hpo.run()

Raw results

Smoothed HPO Curves